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Estimation of the COVIMEP Variation in a HCCI Engine

Year 2020, Volume: 23 Issue: 3, 721 - 727, 01.09.2020
https://doi.org/10.2339/politeknik.567865

Abstract

In this study,
variation of the COVIMEP was tried to be predicted by using the
artificial neural network method for 4-stroke, 4-cylinder, direct injection and
supercharged HCCI engine experimental data obtained by using n-heptane fuel at
60 oC intake air temperature, 1000 rpm engine speed at different
inlet air intake pressure. Intake air inlet pressure and lambda were used as
input data in artificial neural network model. The COVIMEP value was
used as the target. Three layers and five neurons were used to construct the
network using the Levenberg-Marquardt algorithm. Correlation between targets
and outputs for teaching, accuracy and testing were obtained as 0.97989, 0.9504
and 0.91644, respectively. Total correlation factor was found as 0.96983. As a
result of the study, it was seen that the stored data and the estimated COVIMEP
data were compatible.

References

  • [1] Zhao, H. (2007). HCCI and CAI engines for the automotive industry. Woodhead Publishing Ltd., Cambridge England.
  • [2] Heywood, J. B. (1988). Internal combustion engine Fundamentals. McGraw- Hill, New York.
  • [3] Hasan, M.M., Rahman, M.M., Kadirgama, K. (2015). A review on homogeneous charge compression ignition engine performance using biodiesel-diesel blend as a fuel. International Journal of Automotive and Mechanical Engineering, vol.11, p.2199 - 2211.
  • [4] Hairuddin, A.A., Wandel, A.P., Yusaf, T. (2014). An introduction to a homogeneous charge compression ignition engine. Journal of Mechanical Engineering and Sciences, vol. 7, p.1042-1052.
  • [5] Baumgarter, C. (2006). Mixture formation in internal combustion engines. Springer, Heat and Mass transfer series, p. 253-286.
  • [6] Uyumaz, A. (2014). Investigation of the effects of valve lift in a homogenous charged compression ignition gasoline engine on combustion and performance, Ph. D. Thesis, Gazi University, p. 3-12.
  • [7] Polat, S. (2015). An investigation of the effects of operation parameters on combustion in a HCCI engine, Ph. D. Thesis, Gazi University, p. 4-15.
  • [8] Khandelwal, M., Singh T.N. (2006). Prediction of blast induced ground vibrations and frequency in opencast mine: A neural network approach. Journal of Sound and Vibration, vol.289, no.4-5, p. 711-725.
  • [9] Luger, G.F. (2002). Artifical Intelligence: Structures and Strategies for Complex Problem Solving. 4th edition, Addison-Wesley.
  • [10] Ismail, H.M., Ng, H.K., Queck, C.W., Gan, S. (2012). Artificial neural networks modelling of engine-out responses for a light-duty diesel engine fuelled with biodiesel blends. Applied Energy, vol.92, p.769–777.
  • [11] Rezaei, J., Shahbakhti, M., Bahri, B., Aziz, A.A. (2015). Performance prediction of HCCI engines with oxygenated fuels using artificial neural networks. Applied Energy, vol.138, no.460–473.
  • [12] Nabiyev, V.V., (2016).Yapay Zeka, Seçkin Yayıncılık, Ankara, 2016, pp 598.
  • [13] Marquardt, D., (1963). An Algorithm for Least-Squares Estimation of Nonlinear Parameters, SIAM Journal on Applied Mathematics, Vol. 11, No. 2, June 1963, pp 431–441.
  • [14] Hagan, M.T., and M. Menhaj, ‘Training feed-forward networks with the Marquardt algorithm’, IEEE Transactions on Neural Networks, Vol. 5, No. 6, 1999, pp 989–993, 1994.

Estimation of the COVIMEP Variation in a HCCI Engine

Year 2020, Volume: 23 Issue: 3, 721 - 727, 01.09.2020
https://doi.org/10.2339/politeknik.567865

Abstract

In this study, variation of the COVIMEP was tried to be predicted by using the artificial neural network method for 4-stroke, 4-cylinder, direct injection and supercharged HCCI engine experimental data obtained by using n-heptane fuel at 60 oC intake air temperature, 1000 rpm engine speed at different inlet air intake pressure. Intake air inlet pressure and lambda were used as input data in artificial neural network model. The COVIMEP value was used as the target. Three layers and five neurons were used to construct the network using the Levenberg-Marquardt algorithm. Correlation between targets and outputs for teaching, accuracy and testing were obtained as 0.97989, 0.9504 and 0.91644, respectively. Total correlation factor was found as 0.96983. As a result of the study, it was seen that the stored data and the estimated COVIMEP data were compatible.

References

  • [1] Zhao, H. (2007). HCCI and CAI engines for the automotive industry. Woodhead Publishing Ltd., Cambridge England.
  • [2] Heywood, J. B. (1988). Internal combustion engine Fundamentals. McGraw- Hill, New York.
  • [3] Hasan, M.M., Rahman, M.M., Kadirgama, K. (2015). A review on homogeneous charge compression ignition engine performance using biodiesel-diesel blend as a fuel. International Journal of Automotive and Mechanical Engineering, vol.11, p.2199 - 2211.
  • [4] Hairuddin, A.A., Wandel, A.P., Yusaf, T. (2014). An introduction to a homogeneous charge compression ignition engine. Journal of Mechanical Engineering and Sciences, vol. 7, p.1042-1052.
  • [5] Baumgarter, C. (2006). Mixture formation in internal combustion engines. Springer, Heat and Mass transfer series, p. 253-286.
  • [6] Uyumaz, A. (2014). Investigation of the effects of valve lift in a homogenous charged compression ignition gasoline engine on combustion and performance, Ph. D. Thesis, Gazi University, p. 3-12.
  • [7] Polat, S. (2015). An investigation of the effects of operation parameters on combustion in a HCCI engine, Ph. D. Thesis, Gazi University, p. 4-15.
  • [8] Khandelwal, M., Singh T.N. (2006). Prediction of blast induced ground vibrations and frequency in opencast mine: A neural network approach. Journal of Sound and Vibration, vol.289, no.4-5, p. 711-725.
  • [9] Luger, G.F. (2002). Artifical Intelligence: Structures and Strategies for Complex Problem Solving. 4th edition, Addison-Wesley.
  • [10] Ismail, H.M., Ng, H.K., Queck, C.W., Gan, S. (2012). Artificial neural networks modelling of engine-out responses for a light-duty diesel engine fuelled with biodiesel blends. Applied Energy, vol.92, p.769–777.
  • [11] Rezaei, J., Shahbakhti, M., Bahri, B., Aziz, A.A. (2015). Performance prediction of HCCI engines with oxygenated fuels using artificial neural networks. Applied Energy, vol.138, no.460–473.
  • [12] Nabiyev, V.V., (2016).Yapay Zeka, Seçkin Yayıncılık, Ankara, 2016, pp 598.
  • [13] Marquardt, D., (1963). An Algorithm for Least-Squares Estimation of Nonlinear Parameters, SIAM Journal on Applied Mathematics, Vol. 11, No. 2, June 1963, pp 431–441.
  • [14] Hagan, M.T., and M. Menhaj, ‘Training feed-forward networks with the Marquardt algorithm’, IEEE Transactions on Neural Networks, Vol. 5, No. 6, 1999, pp 989–993, 1994.
There are 14 citations in total.

Details

Primary Language English
Subjects Engineering
Journal Section Research Article
Authors

Seyfi Polat 0000-0002-7196-3053

Hamit Solmaz 0000-0003-0689-6824

Alper Calam 0000-0003-4125-2127

Emre Yılmaz This is me 0000-0002-5653-2079

Publication Date September 1, 2020
Submission Date May 20, 2019
Published in Issue Year 2020 Volume: 23 Issue: 3

Cite

APA Polat, S., Solmaz, H., Calam, A., Yılmaz, E. (2020). Estimation of the COVIMEP Variation in a HCCI Engine. Politeknik Dergisi, 23(3), 721-727. https://doi.org/10.2339/politeknik.567865
AMA Polat S, Solmaz H, Calam A, Yılmaz E. Estimation of the COVIMEP Variation in a HCCI Engine. Politeknik Dergisi. September 2020;23(3):721-727. doi:10.2339/politeknik.567865
Chicago Polat, Seyfi, Hamit Solmaz, Alper Calam, and Emre Yılmaz. “Estimation of the COVIMEP Variation in a HCCI Engine”. Politeknik Dergisi 23, no. 3 (September 2020): 721-27. https://doi.org/10.2339/politeknik.567865.
EndNote Polat S, Solmaz H, Calam A, Yılmaz E (September 1, 2020) Estimation of the COVIMEP Variation in a HCCI Engine. Politeknik Dergisi 23 3 721–727.
IEEE S. Polat, H. Solmaz, A. Calam, and E. Yılmaz, “Estimation of the COVIMEP Variation in a HCCI Engine”, Politeknik Dergisi, vol. 23, no. 3, pp. 721–727, 2020, doi: 10.2339/politeknik.567865.
ISNAD Polat, Seyfi et al. “Estimation of the COVIMEP Variation in a HCCI Engine”. Politeknik Dergisi 23/3 (September 2020), 721-727. https://doi.org/10.2339/politeknik.567865.
JAMA Polat S, Solmaz H, Calam A, Yılmaz E. Estimation of the COVIMEP Variation in a HCCI Engine. Politeknik Dergisi. 2020;23:721–727.
MLA Polat, Seyfi et al. “Estimation of the COVIMEP Variation in a HCCI Engine”. Politeknik Dergisi, vol. 23, no. 3, 2020, pp. 721-7, doi:10.2339/politeknik.567865.
Vancouver Polat S, Solmaz H, Calam A, Yılmaz E. Estimation of the COVIMEP Variation in a HCCI Engine. Politeknik Dergisi. 2020;23(3):721-7.